Package index • mgcv

All functions
`betar()`	GAM beta regression family
`FFdes()`	Level 5 fractional factorial designs
`NCV`	Neighbourhood Cross Validation
`Predict.matrix()` `Predict.matrix2()`	Prediction methods for smooth terms in a GAM
`Predict.matrix(<cr.smooth>)` `Predict.matrix(<cs.smooth>)` `Predict.matrix(<cyclic.smooth>)` `Predict.matrix(<pspline.smooth>)` `Predict.matrix(<tensor.smooth>)` `Predict.matrix(<tprs.smooth>)` `Predict.matrix(<ts.smooth>)` `Predict.matrix(<t2.smooth>)`	Predict matrix method functions
`Predict.matrix(<soap.film>)` `Predict.matrix(<sf.wiggly>)` `Predict.matrix(<sf.film>)`	Prediction matrix for soap film smooth
`Rrank()`	Find rank of upper triangular matrix
`Sl.inirep()` `Sl.initial.repara()`	Re-parametrizing model matrix X
`Sl.repara()`	Applying re-parameterization from log-determinant of penalty matrix to model matrix.
`Sl.setup()`	Setting up a list representing a block diagonal penalty matrix
`Tweedie()` `tw()`	GAM Tweedie families
`XWXd()` `XWyd()` `Xbd()` `diagXVXd()` `ijXVXd()`	Internal functions for discretized model matrix handling
`anova(<gam>)` `print(<anova.gam>)`	Approximate hypothesis tests related to GAM fits
`bam()`	Generalized additive models for very large datasets
`bam.update()`	Update a strictly additive bam model for new data.
`bandchol()`	Choleski decomposition of a band diagonal matrix
`bcg()`	Censored Box-Cox Gaussian family
`blas.thread.test()`	BLAS thread safety
`bug.reports.mgcv`	Reporting mgcv bugs.
`cSplineDes()`	Evaluate cyclic B spline basis
`choldrop()` `cholup()`	Deletion and rank one Cholesky factor update
`choose.k`	Basis dimension choice for smooths
`clog()`	GAM censored logistic distribution family for log-logistic AFT models
`cnorm()`	GAM censored normal family for log-normal AFT and Tobit models
`columb` `columb.polys`	Reduced version of Columbus OH crime data
`concurvity()`	GAM concurvity measures
`cox.ph()`	Additive Cox Proportional Hazard Model
`cox.pht`	Additive Cox proportional hazard models with time varying covariates
`cpois()`	GAM censored Poisson family
`dDeta()`	Obtaining derivative w.r.t. linear predictor
`dpnorm()`	Stable evaluation of difference between normal c.d.f.s
`dppois()`	Stable evaluation of difference between Poisson c.d.f.s
`exclude.too.far()`	Exclude prediction grid points too far from data
`extract.lme.cov()` `extract.lme.cov2()`	Extract the data covariance matrix from an lme object
`factor.smooth.interaction` `factor.smooth`	Factor smooth interactions in GAMs
`family.mgcv`	Distribution families in mgcv
`fix.family.link()` `fix.family.var()` `fix.family.ls()` `fix.family.qf()` `fix.family.rd()`	Modify families for use in GAM fitting and checking
`fixDependence()`	Detect linear dependencies of one matrix on another
`formXtViX()`	Form component of GAMM covariance matrix
`formula(<gam>)`	GAM formula
`fs.test()` `fs.boundary()`	FELSPLINE test function
`gam()`	Generalized additive models with integrated smoothness estimation
`gam.check()`	Some diagnostics for a fitted gam model
`gam.control()`	Setting GAM fitting defaults
`gam.convergence` `gam.performance`	GAM convergence and performance issues
`gam.fit()`	GAM P-IRLS estimation with GCV/UBRE smoothness estimation (deprecated)
`gam.fit3()`	P-IRLS GAM estimation with GCV, UBRE/AIC or RE/ML derivative calculation
`gam.fit5.post.proc()`	Post-processing output of gam.fit5
`gam.mh()`	Simple posterior simulation with gam fits
`gam.models`	Specifying generalized additive models
`gam.outer()`	Minimize GCV or UBRE score of a GAM using `outer' iteration
`gam.reparam()`	Finding stable orthogonal re-parameterization of the square root penalty.
`gam.scale`	Scale parameter estimation in GAMs
`gam.selection`	Generalized Additive Model Selection
`gam.side()`	Identifiability side conditions for a GAM
`gam.vcomp()` `print(<gam.vcomp>)`	Report gam smoothness estimates as variance components
`gam2objective()` `gam2derivative()`	Objective functions for GAM smoothing parameter estimation
`gamObject`	Fitted gam object
`gamSim()`	Simulate example data for GAMs
`gamlss.etamu()`	Transform derivatives wrt mu to derivatives wrt linear predictor
`gamlss.gH()`	Calculating derivatives of log-likelihood wrt regression coefficients
`gamm()`	Generalized Additive Mixed Models
`gammals()`	Gamma location-scale model family
`gaulss()`	Gaussian location-scale model family
`get.var()`	Get named variable or evaluate expression from list or data.frame
`gevlss()`	Generalized Extreme Value location-scale model family
`gfam()`	Grouped families
`ginla()`	GAM Integrated Nested Laplace Approximation Newton Enhanced
`gumbls()`	Gumbel location-scale model family
`identifiability`	Identifiability constraints
`in.out()`	Which of a set of points lie within a polygon defined region
`inSide()`	Are points inside boundary?
`influence(<gam>)`	Extract the diagonal of the influence/hat matrix for a GAM
`initial.sp()`	Starting values for multiple smoothing parameter estimation
`interpret.gam()`	Interpret a GAM formula
`jagam()` `sim2jam()`	Just Another Gibbs Additive Modeller: JAGS support for mgcv.
`k.check()`	Checking smooth basis dimension
`ldTweedie()`	Log Tweedie density evaluation
`ldetS()`	Getting log generalized determinant of penalty matrices
`linear.functional.terms` `function.predictors` `signal.regression`	Linear functionals of a smooth in GAMs
`logLik(<gam>)`	AIC and Log likelihood for a fitted GAM
`lp()` `feasible()`	Basic linear programming
`ls.size()`	Size of list elements
`magic()`	Stable Multiple Smoothing Parameter Estimation by GCV or UBRE
`magic.post.proc()`	Auxilliary information from magic fit
`mchol()`	Sparse `chol` function
`mgcv.FAQ`	Frequently Asked Questions for package mgcv
`mgcv.package` `mgcv-package` `mgcv`	Mixed GAM Computation Vehicle with GCV/AIC/REML/NCV smoothness estimation and GAMMs by REML/PQL
`mgcv.parallel`	Parallel computation in mgcv.
`mini.roots()`	Obtain square roots of penalty matrices
`missing.data`	Missing data in GAMs
`model.matrix(<gam>)`	Extract model matrix from GAM fit
`mono.con()`	Monotonicity constraints for a cubic regression spline
`mroot()`	Smallest square root of matrix
`multinom()`	GAM multinomial logistic regression
`mvn()`	Multivariate normal additive models
`negbin()` `nb()`	GAM negative binomial families
`new.name()`	Obtain a name for a new variable that is not already in use
`notExp()` `notLog()`	Functions for better-than-log positive parameterization
`notExp2()` `notLog2()`	Alternative to log parameterization for variance components
`null.space.dimension()`	The basis of the space of un-penalized functions for a TPRS
`ocat()`	GAM ordered categorical family
`one.se.rule`	The one standard error rule for smoother models
`pcls()`	Penalized Constrained Least Squares Fitting
`pdIdnot()`	Overflow proof pdMat class for multiples of the identity matrix
`pdTens()`	Functions implementing a pdMat class for tensor product smooths
`pen.edf()`	Extract the effective degrees of freedom associated with each penalty in a gam fit
`place.knots()`	Automatically place a set of knots evenly through covariate values
`plot(<gam>)`	Default GAM plotting
`polys.plot()`	Plot geographic regions defined as polygons
`predict(<bam>)`	Prediction from fitted Big Additive Model model
`predict(<gam>)`	Prediction from fitted GAM model
`print(<gam>)`	Print a Generalized Additive Model object.
`psum.chisq()`	Evaluate the c.d.f. of a weighted sum of chi-squared deviates
`qq.gam()`	QQ plots for gam model residuals
`rTweedie()`	Generate Tweedie random deviates
`random.effects`	Random effects in GAMs
`residuals(<gam>)`	Generalized Additive Model residuals
`rig()`	Generate inverse Gaussian random deviates
`rmvn()` `r.mvt()` `dmvn()` `d.mvt()`	Generate from or evaluate multivariate normal or t densities.
`s()`	Defining smooths in GAM formulae
`scasm()`	Shape constrained additive smooth models
`scat()`	GAM scaled t family for heavy tailed data
`sdiag()` `sdiag<-`()	Extract or modify diagonals of a matrix
`shash()`	Sinh-arcsinh location scale and shape model family
`single.index`	Single index models with mgcv
`slanczos()`	Compute truncated eigen decomposition of a symmetric matrix
`smooth.construct()` `smooth.construct2()`	Constructor functions for smooth terms in a GAM
`smooth.construct(<ad.smooth.spec>)` `smooth.construct(<scad.smooth.spec>)`	Adaptive smooths in GAMs
`smooth.construct(<bs.smooth.spec>)` `smooth.construct(<sc.smooth.spec>)` `Predict.matrix(<Bspline.smooth>)`	Penalized B-splines in GAMs
`smooth.construct(<cr.smooth.spec>)` `smooth.construct(<cs.smooth.spec>)` `smooth.construct(<cc.smooth.spec>)`	Penalized Cubic regression splines in GAMs
`smooth.construct(<ds.smooth.spec>)` `Predict.matrix(<duchon.spline>)`	Low rank Duchon 1977 splines
`smooth.construct(<fs.smooth.spec>)` `Predict.matrix(<fs.interaction>)`	Factor smooth interactions in GAMs
`smooth.construct(<gp.smooth.spec>)` `Predict.matrix(<gp.smooth>)`	Low rank Gaussian process smooths
`smooth.construct(<mrf.smooth.spec>)` `Predict.matrix(<mrf.smooth>)`	Markov Random Field Smooths
`smooth.construct(<ps.smooth.spec>)` `smooth.construct(<cp.smooth.spec>)`	P-splines in GAMs
`smooth.construct(<re.smooth.spec>)` `Predict.matrix(<random.effect>)`	Simple random effects in GAMs
`smooth.construct(<so.smooth.spec>)` `smooth.construct(<sf.smooth.spec>)` `smooth.construct(<sw.smooth.spec>)`	Soap film smoother constructer
`smooth.construct(<sos.smooth.spec>)` `Predict.matrix(<sos.smooth>)`	Splines on the sphere
`smooth.construct(<sz.smooth.spec>)` `Predict.matrix(<sz.interaction>)`	Constrained factor smooth interactions in GAMs
`smooth.construct(<t2.smooth.spec>)`	Tensor product smoothing constructor
`smooth.construct(<tensor.smooth.spec>)`	Tensor product smoothing constructor
`smooth.construct(<tp.smooth.spec>)` `smooth.construct(<ts.smooth.spec>)`	Penalized thin plate regression splines in GAMs
`smooth.info()`	Generic function to provide extra information about smooth specification
`smooth.terms` `smooths`	Smooth terms in GAM
`smooth2random()`	Convert a smooth to a form suitable for estimating as random effect
`smoothCon()` `PredictMat()`	Prediction/Construction wrapper functions for GAM smooth terms
`sp.vcov()`	Extract smoothing parameter estimator covariance matrix from (RE)ML GAM fit
`spasm.construct()` `spasm.sp()` `spasm.smooth()`	Experimental sparse smoothers
`step.gam`	Alternatives to step.gam
`summary(<gam>)` `print(<summary.gam>)`	Summary for a GAM fit
`t2()`	Define alternative tensor product smooths in GAM formulae
`te()` `ti()`	Define tensor product smooths or tensor product interactions in GAM formulae
`tensor.prod.model.matrix()` `tensor.prod.penalties()` `%.%`	Row Kronecker product/ tensor product smooth construction
`totalPenaltySpace()`	Obtaining (orthogonal) basis for null space and range of the penalty matrix
`trichol()`	Choleski decomposition of a tri-diagonal matrix
`trind.generator()`	Generates index arrays for upper triangular storage
`twlss()`	Tweedie location scale family
`uniquecombs()`	find the unique rows in a matrix
`vcov(<gam>)`	Extract parameter (estimator) covariance matrix from GAM fit
`vis.gam()`	Visualization of GAM objects
`ziP()`	GAM zero-inflated (hurdle) Poisson regression family
`ziplss()` `zipll()`	Zero inflated (hurdle) Poisson location-scale model family

Reference

All functions